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Nataliya Sokolovska
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2020 – today
- 2024
- [j13]Ariane Marandon, Tabea Rebafka, Nataliya Sokolovska, Hédi Soula:
Conformal novelty detection for multiple metabolic networks. BMC Bioinform. 25(1): 358 (2024) - [c26]Margot Herin, Patrice Perny, Nataliya Sokolovska:
Learning GAI-Decomposable Utility Models for Multiattribute Decision Making. AAAI 2024: 20412-20419 - [c25]Margot Herin, Patrice Perny, Nataliya Sokolovska:
Noise-Tolerant Active Preference Learning for Multicriteria Choice Problems. ADT 2024: 191-206 - [c24]Margot Herin, Patrice Perny, Nataliya Sokolovska:
Online Learning of Capacity-Based Preference Models. IJCAI 2024: 7118-7126 - 2023
- [j12]Elie-Julien El Hachem
, Nataliya Sokolovska, Hédi Soula:
Latent dirichlet allocation for double clustering (LDA-DC): discovering patients phenotypes and cell populations within a single Bayesian framework. BMC Bioinform. 24(1): 61 (2023) - [j11]Arsen Sultanov, Jean-Claude Crivello
Data-Driven Score-Based Models for Generating Stable Structures with Adaptive Crystal Cells. J. Chem. Inf. Model. 63(22): 6986-6997 (2023) - [c23]Margot Herin, Patrice Perny, Nataliya Sokolovska:
Learning Preference Models with Sparse Interactions of Criteria. IJCAI 2023: 3786-3794 - [i7]Arsen Sultanov, Jean-Claude Crivello, Tabea Rebafka, Nataliya Sokolovska:
Data-Driven Score-Based Models for Generating Stable Structures with Adaptive Crystal Cells. CoRR abs/2310.10695 (2023) - 2022
- [c22]Margot Herin, Patrice Perny, Nataliya Sokolovska:
Learning sparse representations of preferences within Choquet expected utility theory. UAI 2022: 800-810 - 2021
- [j10]Nataliya Sokolovska
The Role of Instrumental Variables in Causal Inference Based on Independence of Cause and Mechanism. Entropy 23(8): 928 (2021) - [j9]Tatiana Shpakova
Probabilistic personalised cascade with abstention. Pattern Recognit. Lett. 147: 8-15 (2021) - [j8]Nataliya Sokolovska, Yasser Mohseni Behbahani:
Vanishing boosted weights: A consistent algorithm to learn interpretable rules. Pattern Recognit. Lett. 152: 63-69 (2021) - 2020
- [j7]Nataliya Sokolovska
Using Unlabeled Data to Discover Bivariate Causality with Deep Restricted Boltzmann Machines. IEEE ACM Trans. Comput. Biol. Bioinform. 17(1): 358-364 (2020) - [c21]Khaled Belahcène, Nataliya Sokolovska, Yann Chevaleyre, Jean-Daniel Zucker:
Learning Interpretable Models using Soft Integrity Constraints. ACML 2020: 529-544 - [c20]Asma Atamna, Nataliya Sokolovska, Jean-Claude Crivello
A Principled Approach to Analyze Expressiveness and Accuracy of Graph Neural Networks. IDA 2020: 27-39 - [i6]Nataliya Sokolovska, Pierre-Henri Wuillemin
Latent Instrumental Variables as Priors in Causal Inference based on Independence of Cause and Mechanism. CoRR abs/2007.08812 (2020) - [i5]Jean-Claude Crivello
Supervised deep learning prediction of the formation enthalpy of the full set of configurations in complex phases: the σ-phase as an example. CoRR abs/2011.10883 (2020)
2010 – 2019
- 2019
- [j6]Adèle Weber Zendrera, Nataliya Sokolovska
Robust structure measures of metabolic networks that predict prokaryotic optimal growth temperature. BMC Bioinform. 20(1): 499:1-499:13 (2019) - [j5]Nataliya Sokolovska, Karine Clément, Jean-Daniel Zucker
Revealing causality between heterogeneous data sources with deep restricted Boltzmann machines. Inf. Fusion 50: 139-147 (2019) - [c19]Asma Nouira, Nataliya Sokolovska, Jean-Claude Crivello:
CrystalGAN: Learning to Discover Crystallographic Structures with Generative Adversarial Networks. AAAI Spring Symposium: Combining Machine Learning with Knowledge Engineering 2019 - [c18]Matthieu Clertant, Nataliya Sokolovska, Yann Chevaleyre, Blaise Hanczar:
Interpretable Cascade Classifiers with Abstention. AISTATS 2019: 2312-2320 - [c17]Thanh Hai Nguyen
Disease Prediction Using Synthetic Image Representations of Metagenomic Data and Convolutional Neural Networks. RIVF 2019: 1-6 - 2018
- [c16]Nataliya Sokolovska, Yann Chevaleyre, Jean-Daniel Zucker:
A Provable Algorithm for Learning Interpretable Scoring Systems. AISTATS 2018: 566-574 - [c15]Malika Kharouf, Tabea Rebafka, Nataliya Sokolovska:
Consistent Spectral Methods for Dimensionality Reduction. EUSIPCO 2018: 286-290 - [c14]Nataliya Sokolovska, Olga Permiakova, Sofia K. Forslund, Jean-Daniel Zucker
A Semi-supervised Approach to Discover Bivariate Causality in Large Biological Data. MLDM (1) 2018: 406-420 - [c13]Nataliya Sokolovska, Yann Chevaleyre, Jean-Daniel Zucker
Risk Scores Learned by Deep Restricted Boltzmann Machines with Trained Interval Quantization. MLDM (1) 2018: 421-435 - [i4]Thanh Hai Nguyen
Disease Classification in Metagenomics with 2D Embeddings and Deep Learning. CoRR abs/1806.09046 (2018) - [i3]Asma Nouira, Jean-Claude Crivello
CrystalGAN: Learning to Discover Crystallographic Structures with Generative Adversarial Networks. CoRR abs/1810.11203 (2018) - 2017
- [c12]Séverine Affeldt
Efficient global network learning from local reconstructions. IJCNN 2017: 1117-1124 - [c11]Nataliya Sokolovska, Yann Chevaleyre, Karine Clément, Jean-Daniel Zucker
The fused lasso penalty for learning interpretable medical scoring systems. IJCNN 2017: 4504-4511 - [i2]Thanh Hai Nguyen
Deep Learning for Metagenomic Data: using 2D Embeddings and Convolutional Neural Networks. CoRR abs/1712.00244 (2017) - 2016
- [j4]Séverine Affeldt
Spectral consensus strategy for accurate reconstruction of large biological networks. BMC Bioinform. 17(S-16): 85-97 (2016) - [j3]Nataliya Sokolovska, Karine Clément, Jean-Daniel Zucker
Deep kernel dimensionality reduction for scalable data integration. Int. J. Approx. Reason. 74: 121-132 (2016) - [c10]Nataliya Sokolovska, Thierry Artières
A probabilistic prior knowledge integration method: Application to generative and discriminative models. IJCNN 2016: 4496-4503 - [c9]Nataliya Sokolovska, Nguyen Thanh Hai
Deep Self-Organising Maps for efficient heterogeneous biomedical signatures extraction. IJCNN 2016: 5079-5086 - 2015
- [c8]Nataliya Sokolovska, Salwa Rizkalla, Karine Clément, Jean-Daniel Zucker
Continuous and Discrete Deep Classifiers for Data Integration. IDA 2015: 264-274 - 2012
- [c7]Nataliya Sokolovska:
Sparse Gradient-Based Direct Policy Search. ICONIP (4) 2012: 212-221 - 2011
- [c6]Rémi Coulom, Philippe Rolet, Nataliya Sokolovska, Olivier Teytaud:
Handling expensive optimization with large noise. FOGA 2011: 61-68 - [c5]Nataliya Sokolovska, Olivier Teytaud, Mario Milone:
Q-Learning with Double Progressive Widening: Application to Robotics. ICONIP (3) 2011: 103-112 - [c4]Adrien Couëtoux, Jean-Baptiste Hoock, Nataliya Sokolovska, Olivier Teytaud, Nicolas Bonnard:
Continuous Upper Confidence Trees. LION 2011: 433-445 - [c3]Nataliya Sokolovska:
Aspects of Semi-supervised and Active Learning in Conditional Random Fields. ECML/PKDD (3) 2011: 273-288 - 2010
- [b1]Nataliya Sokolovska:
Contributions to the estimation of probabilistic discriminative models: semi-supervised learning and feature selection. (Contributions à l'estimation de modèles probabilistes discriminants: apprentissage semi-supervisé et sélection de caractéristiques). Télécom ParisTech, France, 2010 - [j2]Nataliya Sokolovska, Thomas Lavergne, Olivier Cappé, François Yvon
Efficient Learning of Sparse Conditional Random Fields for Supervised Sequence Labeling. IEEE J. Sel. Top. Signal Process. 4(6): 953-964 (2010) - [c2]Romaric Gaudel, Jean-Baptiste Hoock, Julien Perez, Nataliya Sokolovska, Olivier Teytaud:
A Principled Method for Exploiting Opening Books. Computers and Games 2010: 136-144
2000 – 2009
- 2009
- [j1]Nataliya Sokolovska, Olivier Cappé, François Yvon:
Selecting features with L1 regularization in Conditional Random Fields. Trait. Autom. des Langues 50(3): 139-171 (2009) - [i1]Nataliya Sokolovska, Thomas Lavergne, Olivier Cappé, François Yvon:
Efficient Learning of Sparse Conditional Random Fields for Supervised Sequence Labelling. CoRR abs/0909.1308 (2009) - 2008
- [c1]Nataliya Sokolovska, Olivier Cappé, François Yvon:
The asymptotics of semi-supervised learning in discriminative probabilistic models. ICML 2008: 984-991
Coauthor Index
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